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+* ideas of the day
+
+we will start off with the init function.
+
+go to Wirth's book "programs = data structures + algorithms",
+read it as an example of an exported thought or idea or even an abstract meme.
+
+The export function starts with the Definition of terms.
+
+The export function applied to the export function.
+
+export format definition using standard EBNF
+notation that can be generated by a computer.
+
+
+Next we will say that the rules to translate that grammar into
+a memory structure in the computer
+
+introspection is like an export format,
+because each view of inside is a evolving snapshot
+at each instruction of the processor.
+
+
+lets consider that we are in the process of doing something.
+
+reflecting over our own process might changed its direction.
+
+a self centered, self focused, introspective, inward view of ourselves,
+internal access, deep neural embedding, higher order being, carried proof,
+carried truth, the king in his carriage, the baby in his stroller,
+basic awareness being carried by neurons, self directed learning,
+unsupervised deep learning neural networks,
+autopoetic wild thoughts.
+
+let consider this to be a huge private network we are attached to in our mind.
+we can imagine things, we can imagine ourselves imagining,
+we can recall ourselves imagining,
+we can recall ourselves reading,
+we can recall ourselves hearing,
+we can recall ourselves seeing,
+we can recall ourselves recalling,
+we can recall ourselves learning to observe ourselves,
+we can recall ourselves learning to control ourselves,
+we can recall our parents learning to control ourselves,
+we can recall our environment controlling us,
+we can recall our environment shaping us,
+
+now we can think of this as supervised learning and pre training,
+structured learning.
+
+so this supervision or control flow is quite important.
+lets look at the chain of custody and ownership records
+of each bit of information.
+
+If we know where each neural imprint came from, what its purpose is,
+why it is needed, its functional equivalents,
+then we can model the purpose of on network with another.
+
+if any neural network of capacity N named X can approximate any
+function of capacity Y
+then we have a sort of carrying relationship,
+so "this network X of size N carries a function of binding depth size Y".
+
+Now we need to work on our notation a bit.
+we can recognize now a function size can be its binding depth,
+or how many arguments it needs. we can think of partial applications.
+
+what if we think of objects as partial applications where each
+neural record or attribute  or property or terminal record represents
+a phenomena captured from outside.
+so terms represent atomic things that can be sampled by edge networks.
+
+we can think of a zero trust security policy as securing the edge network
+in the interest of the company as a deployment of a foundational
+model of a partial application.
+We can study the landing pads of GCP google and azure microsoft
+as doorways into those systems.
+
+We can look at price quoting systems of markets as standardized
+vectors of information.
+Atomic swaps of ownership, transactions.
+We can see creating a token as buying into a blockchain.
+you first need to purchase capacity on that network,
+so run a node.
+We can look at GNU net + guix + cuirass as a partial application.
+
+Now we can consider each let binding as a partial application of an ever changing
+binding of values.
+
+